Papers with models risks encoding

1 papers
CAPE: Context-Aware Private Embeddings for Private Language Learning (2021.emnlp-main)

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Challenge: Existing methods to obtain text representations or embeddings with these models encoding personally identifiable information may lead to privacy leaks.
Approach: They propose a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings.
Outcome: The proposed approach reduces private information leakage by 3% over the current method.

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